Discovering semantic features in the literature: a foundation for building functional associations.

ABSTRACT:

Background

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.

Results

We present a method to create literature profiles for large sets of genes or proteins based on common sema

SUBMITTER: Chagoyen M 

PROVIDER: S-EPMC1386711 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature